Typically, when making recommendations on various systems (e.g. e-consumption sites, etc.) for digital content (e.g. news, music, videos, movies, and etc.), such systems must find the best matches between a supply (e.g. news items) and a demand (e.g. what items a particular user is looking for or items of interest), and then make a recommendation. To date, such systems are quite limited. Just by way of example, they are: generally static in nature, incapable of effectively making recommendations at start-up in the absence of any information on a behavior of a particular user, and/or incapable of effectively leveraging a Direchlet distribution in the context of content category ranking since Direchlet distribution parameters are generally not available.